Showing papers by "Baskar Ganapathysubramanian published in 2023"
TL;DR: In this paper , the authors integrate the electrokinetic pre-enrichment of nucleic acids within a bed of probe-modified microbeads with their label-free electrochemical detection.
Abstract: In this paper, we report a method to integrate the electrokinetic pre-enrichment of nucleic acids within a bed of probe-modified microbeads with their label-free electrochemical detection. In this detection scheme, hybridization of locally enriched target nucleic acids to the beads modulates the conduction of ions along the bead surfaces. This is a fundamental advancement in that this mechanism is similar to that observed in nanopore sensors, yet occurs in a bed of microbeads with microscale interstices. In application, this approach has several distinct advantages. First, electrokinetic enrichment requires only a simple DC power supply, and in combination with nonoptical detection, it makes this method amenable to point-of-care applications. Second, the sensor is easy to fabricate and comprises a packed bed of commercially available microbeads, which can be readily modified with a wide range of probe types, thereby making this a versatile platform. Finally, the sensor is highly sensitive (picomolar) despite the modest 100-fold pre-enrichment we employ here by faradaic ion concentration polarization (fICP). Further gains are anticipated under conditions for fICP focusing that are known to yield higher enrichment factors (up to 100,000-fold enrichment). Here, we demonstrate the detection of 3.7 pM single-stranded DNA complementary to the bead-bound oligoprobe, following a 30 min single step of enrichment and hybridization. Our results indicate that a shift in the slope of a current-voltage curve occurs upon hybridization and that this shift is proportional to the logarithm of the concentration of target DNA. Finally, we investigate the proposed mechanism of sensing by developing a numerical simulation that shows an increase in ion flux through the bed of insulating beads, given the changes in surface charge and zeta potential, consistent with our experimental conditions.
3 citations
TL;DR: In this article , a regression model was developed to predict each ovate leaflet's area (adjusted R2 = 0.97; residual standard errors of < = 1.10).
Abstract: Mungbean (Vigna radiata (L.) Wizcek) is an important pulse crop, increasingly used as a source of protein, fiber, low fat, carbohydrates, minerals, and bioactive compounds in human diets. Mungbean is a dicot plant with trifoliate leaves. The primary component of many plant functions, including photosynthesis, light interception, and canopy structure, are leaves. The objectives were to investigate leaf morphological attributes, use image analysis to extract leaf morphological traits from photos from the Iowa Mungbean Diversity (IMD) panel, create a regression model to predict leaflet area, and undertake association mapping. We collected over 5000 leaf images of the IMD panel consisting of 484 accessions over 2 years (2020 and 2021) with two replications per experiment. Leaf traits were extracted using image analysis, analyzed, and used for association mapping. Morphological diversity included leaflet type (oval or lobed), leaflet size (small, medium, large), lobed angle (shallow, deep), and vein coloration (green, purple). A regression model was developed to predict each ovate leaflet's area (adjusted R2 = 0.97; residual standard errors of < = 1.10). The candidate genes Vradi01g07560, Vradi05g01240, Vradi02g05730, and Vradi03g00440 are associated with multiple traits (length, width, perimeter, and area) across the leaflets (left, terminal, and right). These are suitable candidate genes for further investigation in their role in leaf development, growth, and function. Future studies will be needed to correlate the observed traits discussed here with yield or important agronomic traits for use as phenotypic or genotypic markers in marker-aided selection methods for mungbean crop improvement.
2 citations
TL;DR: In this article , a deep learning model called "InsectNet" is proposed to identify the full insect community, including beneficial insects and harmful pests, to develop and implement integrated pest management strategies.
Abstract: Insect-pests significantly impact global agricultural productivity and quality. Effective management involves identifying the full insect community, including beneficial insects and harmful pests, to develop and implement integrated pest management strategies. Automated identification of insects under real-world conditions presents several challenges, including differentiating similar-looking species, intra-species dissimilarity and inter-species similarity, several life cycle stages, camouflage, diverse imaging conditions, and variability in insect orientation. A deep-learning model, InsectNet, is proposed to address these challenges. InsectNet is endowed with five key features: (a) utilization of a large dataset of insect images collected through citizen science; (b) label-free self-supervised learning for large models; (c) improving prediction accuracy for species with a small sample size; (d) enhancing model trustworthiness; and (e) democratizing access through streamlined MLOps. This approach allows accurate identification (>96% accuracy) of over 2500 insect species, including pollinator (e.g., butterflies, bees), parasitoid (e.g., some wasps and flies), predator species (e.g., lady beetles, mantises, dragonflies) and harmful pest species (e.g., armyworms, cutworms, grasshoppers, stink bugs). InsectNet can identify invasive species, provide fine-grained insect species identification, and work effectively in challenging backgrounds. It also can abstain from making predictions when uncertain, facilitating seamless human intervention and making it a practical and trustworthy tool. InsectNet can guide citizen science data collection, especially for invasive species where early detection is crucial. Similar approaches may transform other agricultural challenges like disease detection and underscore the importance of data collection, particularly through citizen science efforts..
TL;DR: In this article , a cluster-based ensemble approach for yield prediction was proposed for large-scale breeding programs by efficiently harnessing useful information from data through an unsupervised approach.
Abstract: Soybean yield prediction is a challenging problem in plant breeding that is often affected by many different factors simultaneously. Hyperspectral reflectance data from plants provide breeders with useful data about the health of soybean plants and using this data for yield prediction is an active area of research. Often breeding programs suffer from issues such as data imbalance and several external factors such as genotype variablility in different environments which can pose a serious challenge for developing yield prediction models for large scale breeding programs. In this work we demonstrate a cluster based ensemble approach for yield prediction that can perform well for large scale breeding programs by efficiently harnessing useful information from data through an unsupervised approach.
TL;DR: In this article , the authors propose a general framework for creating a digital twin of the dynamic printing process by performing physics simulations with the intermediate print geometries, which can predict the transient heat distribution as the print progresses.
Abstract: Accurate simulation of the printing process is essential for improving print quality, reducing waste, and optimizing the printing parameters of extrusion-based additive manufacturing. Traditional additive manufacturing simulations are very compute-intensive and are not scalable to simulate even moderately-sized geometries. In this paper, we propose a general framework for creating a digital twin of the dynamic printing process by performing physics simulations with the intermediate print geometries. Our framework takes a general extrusion-based additive manufacturing G-code, generates an analysis-suitable voxelized geometry representation from the print schedule, and performs physics-based (transient thermal and phase change) simulations of the printing process. Our approach leverages parallel adaptive octree meshes for both voxelated geometry representation as well as for fast simulations to address real-time predictions. We demonstrate the effectiveness of our method by simulating the printing of complex geometries at high voxel resolutions with both sparse and dense infills. Our results show that this approach scales to high voxel resolutions and can predict the transient heat distribution as the print progresses. This work lays the computational and algorithmic foundations for building real-time digital twins and performing rapid virtual print sequence exploration to improve print quality and further reduce material waste.
TL;DR: In this paper , a high-level domain-specific language (DSL) interface is presented to drive an adaptive incomplete $k$d tree-based framework for finite element (FEM) solutions to PDEs.
Abstract: We present a high-level domain-specific language (DSL) interface to drive an adaptive incomplete $k$-d tree-based framework for finite element (FEM) solutions to PDEs. This DSL provides three key advances: (a) it abstracts out the complexity of implementing non-trivial FEM formulations, (b) it simplifies deploying these formulations on arbitrarily complicated and adaptively refined meshes, and (c) it exhibits good parallel performance. Taken together, the DSL interface allows end-users to rapidly and efficiently prototype new mathematical approaches, and deploy them on large clusters for solving practical problems. We illustrate this DSL by implementing a workflow for solving PDEs using the recently developed shifted boundary method (SBM). The SBM requires approximating relatively complicated integrals over boundary surfaces. Using a high-level DSL greatly simplifies this process and allows rapid exploration of variations. We demonstrate these tools on a variety of 2-D and 3-D configurations. With fewer than 20 lines of input, we can produce a parallel code that scales well to thousands of processes. This generated code is made accessible and readable to be easily modified and hand-tuned, making this tool useful even to experts with the target software.
TL;DR: In this paper , a variational multiscale (VMS) treatment, a block-iterative strategy in conjunction with semi-implicit (for NS) and implicit (for PNP) time integrators, and an octree based adaptive mesh re-construction are presented.
Abstract: Finite element modeling of charged species transport has enabled the analysis, design, and optimization of a diverse array of electrochemical and electrokinetic devices. These systems are represented by the Poisson-Nernst-Planck (PNP) equations coupled with the Navier-Stokes (NS) equation. Direct numerical simulation (DNS) to accurately capture the spatio-temporal variation of ion concentration and current flux remains challenging due to the (a) small critical dimension of the electric double layer (EDL), (b) stiff coupling, large advective effects, and steep gradients close to boundaries, and (c) complex geometries exhibited by electrochemical devices. In the current study, we address these challenges by presenting a direct numerical simulation framework that incorporates: (a) a variational multiscale (VMS) treatment, (b) a block-iterative strategy in conjunction with semi-implicit (for NS) and implicit (for PNP) time integrators, and (c) octree based adaptive mesh refinement. The VMS formulation provides numerical stabilization critical for capturing the electro-convective instabilities often observed in engineered devices. The block-iterative strategy decouples the difficulty of non-linear coupling between the NS and PNP equations and allows using tailored numerical schemes separately for NS and PNP equations. The carefully designed second-order, hybrid implicit methods circumvent the harsh timestep re-quirements of explicit time steppers, thus enabling simulations over longer time horizons. Finally, the octree-based meshing allows efficient and targeted spatial resolution of the EDL. These features are incorporated into a massively parallel computational framework, enabling the simulation of realistic engineering electrochemical devices. The numerical framework is illustrated using several challenging canonical examples.
TL;DR: In this article , the authors present a workflow for high-throughput construction of a library of phase diagrams, followed by an exploration of the solvent selection space guided by the library.
TL;DR: In this paper , the location where boundary conditions are enforced is shifted from the actual boundary of the immersed object to a nearby surrogate boundary, and boundary condition are corrected utilizing Taylor expansions, which allows choosing surrogate boundaries that conform to a Cartesian mesh without losing accuracy or stability.
Abstract: The accurate and efficient simulation of Partial Differential Equations (PDEs) in and around arbitrarily defined geometries is critical for many application domains. Immersed boundary methods (IBMs) alleviate the usually laborious and time-consuming process of creating body-fitted meshes around complex geometry models (described by CAD or other representations, e.g., STL, point clouds), especially when high levels of mesh adaptivity are required. In this work, we advance the field of IBM in the context of the recently developed Shifted Boundary Method (SBM). In the SBM, the location where boundary conditions are enforced is shifted from the actual boundary of the immersed object to a nearby surrogate boundary, and boundary conditions are corrected utilizing Taylor expansions. This approach allows choosing surrogate boundaries that conform to a Cartesian mesh without losing accuracy or stability. Our contributions in this work are as follows: (a) we show that the SBM numerical error can be greatly reduced by an optimal choice of the surrogate boundary, (b) we mathematically prove the optimal convergence of the SBM for this optimal choice of the surrogate boundary, (c) we deploy the SBM on massively parallel octree meshes, including algorithmic advances to handle incomplete octrees, and (d) we showcase the applicability of these approaches with a wide variety of simulations involving complex shapes, sharp corners, and different topologies. Specific emphasis is given to Poisson's equation and the linear elasticity equations.
TL;DR: In this paper , the performance of state-of-the-art OOD algorithms on insect detection classifiers is evaluated. But, the authors focus on extrusive algorithms, i.e., algorithms that wrap around a well-trained classifier without the need for additional co-training.
Abstract: Deep learning-based approaches have produced models with good insect classification accuracy; Most of these models are conducive for application in controlled environmental conditions. One of the primary emphasis of researchers is to implement identification and classification models in the real agriculture fields, which is challenging because input images that are wildly out of the distribution (e.g., images like vehicles, animals, humans, or a blurred image of an insect or insect class that is not yet trained on) can produce an incorrect insect classification. Out-of-distribution (OOD) detection algorithms provide an exciting avenue to overcome these challenge as it ensures that a model abstains from making incorrect classification prediction of non-insect and/or untrained insect class images. We generate and evaluate the performance of state-of-the-art OOD algorithms on insect detection classifiers. These algorithms represent a diversity of methods for addressing an OOD problem. Specifically, we focus on extrusive algorithms, i.e., algorithms that wrap around a well-trained classifier without the need for additional co-training. We compared three OOD detection algorithms: (i) Maximum Softmax Probability, which uses the softmax value as a confidence score, (ii) Mahalanobis distance-based algorithm, which uses a generative classification approach; and (iii) Energy-Based algorithm that maps the input data to a scalar value, called energy. We performed an extensive series of evaluations of these OOD algorithms across three performance axes: (a) \textit{Base model accuracy}: How does the accuracy of the classifier impact OOD performance? (b) How does the \textit{level of dissimilarity to the domain} impact OOD performance? and (c) \textit{Data imbalance}: How sensitive is OOD performance to the imbalance in per-class sample size?
TL;DR: In this article , the authors developed an end-to-end pipeline to generate canopy fingerprints of a 3D point cloud of soybean [Glycine max (L.) Merr.] canopies grown in hill plots captured by a terrestrial laser scanner (TLS).
Abstract: Advances in imaging hardware allow high throughput capture of the detailed three-dimensional (3D) structure of plant canopies. The point cloud data is typically post-processed to extract coarse-scale geometric features (like volume, surface area, height, etc.) for downstream analysis. We extend feature extraction from 3D point cloud data to various additional features, which we denote as ‘canopy fingerprints’. This is motivated by the successful application of the fingerprint concept for molecular fingerprints in chemistry applications and acoustic fingerprints in sound engineering applications. We developed an end-to-end pipeline to generate canopy fingerprints of a three-dimensional point cloud of soybean [Glycine max (L.) Merr.] canopies grown in hill plots captured by a terrestrial laser scanner (TLS). The pipeline includes noise removal, registration, and plot extraction, followed by the canopy fingerprint generation. The canopy fingerprints are generated by splitting the data into multiple sub-canopy scale components and extracting sub-canopy scale geometric features. The generated canopy fingerprints are interpretable and can assist in identifying patterns in a database of canopies, querying similar canopies, or identifying canopies with a certain shape. The framework can be extended to other modalities (for instance, hyperspectral point clouds) and tuned to find the most informative fingerprint representation for downstream tasks. These canopy fingerprints can aid in the utilization of canopy traits at previously unutilized scales, and therefore have applications in plant breeding and resilient crop production.
TL;DR: In this article , the authors present a full space-time numerical solution of the advection-diffusion equation using a continuous Galerkin finite element method to ensure stability of the discrete variational problem.
Abstract: We present a full space-time numerical solution of the advection-diffusion equation using a continuous Galerkin finite element method. The Galerkin/least-square method is employed to ensure stability of the discrete variational problem. In the full space-time formulation, time is considered another dimension, and the time derivative is interpreted as an additional advection term of the field variable. We derive a priori error estimates and illustrate spatio-temporal convergence with several numerical examples. We also derive a posteriori error estimates, which coupled with adaptive space-time mesh refinement provide efficient and accurate solutions. The accuracy of the space-time solutions is illustrated against analytical solutions as well as against numerical solutions using a conventional time-marching algorithm.
TL;DR: In this article , the authors demonstrate the ability to generate a 3D reconstruction (mesh) of a maize plant by leveraging a recent work in 3D computer vision, Neural Radiance Fields (NeRFs), which uses data collected from a mobile phone camera.
Abstract: Real-time simulations of large-scale farming operations would provide farmers with data-driven and physicsconsistent decision support. These real-time farming simulations could be accomplished using predictive digital twins. Predictive digital twins of biological entities allow for a virtual simulation of real-life processes for various environmental conditions, thus paving the way for a comprehensive understanding of various biological responses. One of the first steps in constructing a predictive digital twin is the 3D reconstruction of plant geometry. While traditional approaches for the reconstruction of plant geometry exist, they require a very expensive setup using a LIDAR or destructive imaging of the plant in a controlled environment. Neural approaches for 3D scene reconstruction have alleviated the data collection burden associated with traditional 3D reconstruction methods. In this work, we demonstrate the ability to generate a 3D reconstruction (mesh) of a maize plant by leveraging a recent work in 3D computer vision, Neural Radiance Fields (NeRFs), which uses data collected from a mobile phone camera. Our approach aims to generate high-resolution geometric models for several downstream tasks, such as developing a predictive